Weather Forecast Analysis Based on ARIMA Model: A Case Study of Stockholm

Haoyu Li

2023

Abstract

This paper presents a comprehensive investigation into the development of a temperature prediction model using the city of Stockholm as a case study. Time series modeling techniques are used in this research to forecast future monthly average temperatures. The dataset used in this study covers a wide range, from January 1980 to December 2020, offering ample historical data for analysis. As the primary forecasting approach, the researchers have selected the Autoregressive Integrated Moving Average (ARIMA) model. To identify the optimal orders for the ARIMA model, an analysis is performed using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots, allowing for accurate determination of the suitable parameters. Furthermore, a comprehensive residual analysis is conducted to verify that the residuals demonstrate the properties of white noise, providing further assurance about the model’s reliability. The obtained results demonstrate that the proposed ARIMA model achieves high prediction accuracy in estimating future monthly average temperatures. Overall, this research contributes to the field of climate prediction by showcasing an effective methodology for temperature forecasting at a local level. By using Stockholm as an example, key patterns and trends specific to the region are identified, highlighting the applicability of the developed model to similar geographical locations.

Paper Citation

in Harvard Style

Li H. (2023). Weather Forecast Analysis Based on ARIMA Model: A Case Study of Stockholm. In Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-705-4, SciTePress, pages 164-170. DOI: 10.5220/0012810300003885

in Bibtex Style

@conference{daml23,
author={Haoyu Li},
title={Weather Forecast Analysis Based on ARIMA Model: A Case Study of Stockholm},
booktitle={Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2023},
pages={164-170},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012810300003885},
isbn={978-989-758-705-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Weather Forecast Analysis Based on ARIMA Model: A Case Study of Stockholm
SN - 978-989-758-705-4
AU - Li H.
PY - 2023
SP - 164
EP - 170
DO - 10.5220/0012810300003885
PB - SciTePress